code: def shorten_words(words, amount, letter_bits):
      """Shorten words."""
      new_words = np.zeros(
          (words.shape[0], words.shape[1]), dtype=np.uint32
      )
  
      # Unigrams
      shift_len = amount * letter_bits
      for j in prange(words.shape[1]):
          # shorten a word by set amount of letters
          new_words[:, j] = words[:, j] >> shift_len
  
      # TODO Bigrams
      # if bigrams:
      #     for a in range(0, n_instances):
      #         first_word = new_words[:, a]
      #         second_word = new_words[:, a + window_size]
      #         words[:, n_instances + a] = (first_word << word_bits) | second_word
  
      return new_words
  
file_path: sktime/sktime/transformations/panel/dictionary_based/_sfa_fast_numba.py
imports: import math
  from warnings import simplefilter
  import numpy
  from sktime.utils.numba.njit import njit
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 13 for calling a global function with example usage: shorten_words(words, amount, letter_bits) and returns: new_words